Machine learning techniques for identifying clouds and cloud shadows in satellite imagery

ABSTRACT

Systems and methods for identifying clouds and cloud shadows in satellite imagery are described herein. In an embodiment, a system receives a plurality of images of agronomic fields produced using one or more frequency bands. The system also receives corresponding data identifying cloud and cloud shadow locations in the images. The system trains. a machine learning system to identify at least cloud locations using the images as inputs and at least data identifying pixels as cloud pixels or non-cloud pixels as outputs. When the system receives one or more particular images of a particular agronomic field produced using the one or more frequency bands, the system uses the one or more particular images as inputs into the machine learning system to identify a plurality of pixels in the one or more particular images as particular cloud locations.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/657,957, filed on Oct. 18, 2019, which claims the priority of U.S.Provisional Application No. 62/748,293, filed Oct. 19, 2018, the entirecontents of each of which are incorporated herein by reference.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyright orrights whatsoever. © 2015-2019 The Climate Corporation.

FIELD OF THE DISCLOSURE

One technical field of the present disclosure is image classificationusing machine learning techniques.

BACKGROUND OF THE DISCLOSURE

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

Satellite imagery of agronomic fields can be extremely useful inagronomic modeling. For example, many agronomic yield models estimatethe agronomic yield of a crop based on vegetative index values that arederived from satellite imagery. Thus, it can be extremely important thatsatellite imagery of a field is accurate as even slight variances invalues can negatively impact the usefulness of the imagery for modelingpurposes or display.

In many cases, clouds in the atmosphere partially or fully obscure asatellite sensor's view of the Earth's surface. The clouds may also castshadows on the ground where less sunlight is reflected to the sensor.Both the clouds and the shadows of the clouds can negatively impact theusefulness of images containing the clouds and cloud shadows. Forinstance, a computation of an average vegetative index value for a fieldbased on pixel values will be inaccurate if some of the pixels aredarker due to the shadows of a cloud and other pixels are brighter dueto the overlay of a shadow.

It is thus imperative in many cases for systems to be able to detectclouds and cloud shadows in satellite imagery. Classification techniqueshave been developed which can identify clouds and cloud shadows withfairly high accuracy. For example, U.S. Pat. No. 9,721,181, the entirecontents of which are incorporated by reference as if fully describedherein, describes a three-step process for identifying clouds and cloudshadow pixels and using the identified pixels to create a cloud mask andshadow mask of a remote sensing image.

Generally, detecting cloud boundaries or thinner clouds can be difficultdue to varying opacity of pixels near the border of clouds or in thinnerclouds. Thus, pixels that should be removed or masked in an image may bemissed.

Thus, there is a need for a system which leverages information regardingsurrounding pixels in an image in order to identify pixels as clouds orcloud shadows.

SUMMARY OF THE DISCLOSURE

The appended claims may serve as a summary of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate.

FIG. 2 illustrates two views of an example logical organization of setsof instructions in main memory when an example mobile application isloaded for execution.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using agronomic data provided by one or more datasources.

FIG. 4 is a block diagram that illustrates a computer system upon whichan embodiment of the invention may be implemented.

FIG. 5 depicts an example embodiment of a timeline view for data entry.

FIG. 6 depicts an example embodiment of a spreadsheet view for dataentry.

FIG. 7 depicts an example method for using a machine learning system toidentify pixels in an image as cloud locations.

FIG. 8 depicts an example convolutional encoder-decoder machine learningsystem.

FIG. 9 depicts a method of using a first machine learning system toidentify pixels in an image as cloud locations and a second machinelearning system to identify pixels in an image as cloud shadowlocations.

FIG. 10 depicts a plurality of images comprising cloud shadow locationsgenerated based on varying assumptions of satellite angle and cloudheight.

DETAILED DESCRIPTION OF THE DISCLOSURE

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present disclosure. It will be apparent, however,that embodiments may be practiced without these specific details. Inother instances, well-known structures and devices are shown in blockdiagram form in order to avoid unnecessarily obscuring the presentdisclosure. Embodiments are disclosed in sections according to thefollowing outline:

1. GENERAL OVERVIEW

2. EXAMPLE AGRICULTURAL INTELLIGENCE COMPUTER SYSTEM

-   -   2.1. STRUCTURAL OVERVIEW    -   2.2. APPLICATION PROGRAM OVERVIEW    -   2.3. DATA INGEST TO THE COMPUTER SYSTEM    -   2.4. PROCESS OVERVIEW—AGRONOMIC MODEL TRAINING    -   2.5. IMPLEMENTATION EXAMPLE—HARDWARE OVERVIEW

3. CLOUD AND CLOUD SHADOW DETECTION SYSTEM

-   -   3.1. RECEIVED DATA    -   3.2. CLOUD IDENTIFYING MACHINE LEARNING SYSTEM    -   3.3. CLOUD AND CLOUD SHADOW SEGMENTATION

4. REPLACING IDENTIFIED CLOUD PIXELS

5. AGRONOMIC MODELING

6. AGRONOMIC MAP DISPLAY

7. BENEFITS OF CERTAIN EMBODIMENTS

1. General Overview

Systems and methods are described for using a machine learning systemthat leverages information regarding classification of surroundingpixels to identify clouds and cloud shadows in an image. In anembodiment, a system receives a plurality of images of agronomic fieldsas well as data identifying pixels as clouds or cloud shadows in theimages. The system trains a machine learning system, such as aconvolutional encoder-decoder, using the images as inputs and the dataidentifying pixels as clouds as outputs. When the system receives animage of any agronomic field, the system uses the machine learningsystem, which has been trained to classify pixels based on surroundingpixel values, to identify pixels in the image as cloud pixels ornon-cloud pixels. In an embodiment, the system additionally usesgeometric techniques to identify candidate cloud shadow locations in thetraining data and trains a second machine learning system to identifycloud shadow pixels using the images and candidate cloud shadowlocations as inputs and data identifying pixels as cloud shadow pixelsas outputs.

In an embodiment, a computer implemented method comprises receiving aplurality of images of agronomic fields produced using one or morefrequency bands; receiving corresponding data identifying cloud andcloud shadow locations in the images; training a machine learning systemto identify at least cloud locations using the images as inputs and atleast data identifying pixels as cloud pixels or non-cloud pixels asoutputs; receiving one or more particular images of a particularagronomic field produced using the one or more frequency bands; usingthe one or more particular images as inputs into the machine learningsystem, identifying a plurality of pixels in the one or more particularimages as particular cloud locations.

In an embodiment, a computer implemented method comprises receiving aplurality of images of agronomic fields produced using one or morefrequency bands; receiving corresponding data identifying cloud andcloud shadow locations in the images; training a first machine learningsystem to identify cloud locations using the images as inputs and dataidentifying pixels as cloud pixels or non-cloud pixels as outputs; usingthe data identifying cloud locations, identifying a plurality ofcandidate cloud shadow locations; training a second machine learningsystem to identify cloud shadow locations using the images and thecandidate cloud shadow locations as inputs and data identifying pixelsas cloud shadow pixels or non-cloud shadow pixels as outputs; receivingone or more particular images of a particular agronomic field producedusing the one or more frequency bands; using the one or more particularimages as inputs into the first machine learning system, identifying aplurality of pixels in the one or more particular images as particularcloud locations; using the particular cloud locations, identifying aplurality of particular candidate cloud shadow locations; using the oneor more particular images and the plurality of particular candidatecloud shadow locations as inputs into the second machine learningsystem, identifying a plurality of pixels in the one or more particularimages as particular cloud shadow locations.

2. Example Agricultural Intelligence Computer System

2.1 Structural Overview

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate. In oneembodiment, a user 102 owns, operates or possesses a field managercomputing device 104 in a field location or associated with a fieldlocation such as a field intended for agricultural activities or amanagement location for one or more agricultural fields. The fieldmanager computer device 104 is programmed or configured to provide fielddata 106 to an agricultural intelligence computer system 130 via one ormore networks 109.

Examples of field data 106 include (a) identification data (for example,acreage, field name, field identifiers, geographic identifiers, boundaryidentifiers, crop identifiers, and any other suitable data that may beused to identify farm land, such as a common land unit (CLU), lot andblock number, a parcel number, geographic coordinates and boundaries,Farm Serial Number (FSN), farm number, tract number, field number,section, township, and/or range), (b) harvest data (for example, croptype, crop variety, crop rotation, whether the crop is grownorganically, harvest date, Actual Production History (APH), expectedyield, yield, crop price, crop revenue, grain moisture, tillagepractice, and previous growing season information), (c) soil data (forexample, type, composition, pH, organic matter (OM), cation exchangecapacity (CEC)), (d) planting data (for example, planting date, seed(s)type, relative maturity (RM) of planted seed(s), seed population), (e)fertilizer data (for example, nutrient type (Nitrogen, Phosphorous,Potassium), application type, application date, amount, source, method),(f) chemical application data (for example, pesticide, herbicide,fungicide, other substance or mixture of substances intended for use asa plant regulator, defoliant, or desiccant, application date, amount,source, method), (g) irrigation data (for example, application date,amount, source, method), (h) weather data (for example, precipitation,rainfall rate, predicted rainfall, water runoff rate region,temperature, wind, forecast, pressure, visibility, clouds, heat index,dew point, humidity, snow depth, air quality, sunrise, sunset), (i)imagery data (for example, imagery and light spectrum information froman agricultural apparatus sensor, camera, computer, smartphone, tablet,unmanned aerial vehicle, planes or satellite), (j) scouting observations(photos, videos, free form notes, voice recordings, voicetranscriptions, weather conditions (temperature, precipitation (currentand over time), soil moisture, crop growth stage, wind velocity,relative humidity, dew point, black layer)), and (k) soil, seed, cropphenology, pest and disease reporting, and predictions sources anddatabases.

A data server computer 108 is communicatively coupled to agriculturalintelligence computer system 130 and is programmed or configured to sendexternal data 110 to agricultural intelligence computer system 130 viathe network(s) 109. The external data server computer 108 may be ownedor operated by the same legal person or entity as the agriculturalintelligence computer system 130, or by a different person or entitysuch as a government agency, non-governmental organization (NGO), and/ora private data service provider. Examples of external data includeweather data, imagery data, soil data, or statistical data relating tocrop yields, among others. External data 110 may consist of the sametype of information as field data 106. In some embodiments, the externaldata 110 is provided by an external data server 108 owned by the sameentity that owns and/or operates the agricultural intelligence computersystem 130. For example, the agricultural intelligence computer system130 may include a data server focused exclusively on a type of data thatmight otherwise be obtained from third party sources, such as weatherdata. In some embodiments, an external data server 108 may actually beincorporated within the system 130.

An agricultural apparatus 111 may have one or more remote sensors 112fixed thereon, which sensors are communicatively coupled either directlyor indirectly via agricultural apparatus 111 to the agriculturalintelligence computer system 130 and are programmed or configured tosend sensor data to agricultural intelligence computer system 130.Examples of agricultural apparatus 111 include tractors, combines,harvesters, planters, trucks, fertilizer equipment, aerial vehiclesincluding unmanned aerial vehicles, and any other item of physicalmachinery or hardware, typically mobile machinery, and which may be usedin tasks associated with agriculture. In some embodiments, a single unitof apparatus 111 may comprise a plurality of sensors 112 that arecoupled locally in a network on the apparatus; controller area network(CAN) is example of such a network that can be installed in combines,harvesters, sprayers, and cultivators. Application controller 114 iscommunicatively coupled to agricultural intelligence computer system 130via the network(s) 109 and is programmed or configured to receive one ormore scripts that are used to control an operating parameter of anagricultural vehicle or implement from the agricultural intelligencecomputer system 130. For instance, a controller area network (CAN) businterface may be used to enable communications from the agriculturalintelligence computer system 130 to the agricultural apparatus 111, suchas how the CLIMATE FIELDVIEW DRIVE, available from The ClimateCorporation, San Francisco, Calif., is used. Sensor data may consist ofthe same type of information as field data 106. In some embodiments,remote sensors 112 may not be fixed to an agricultural apparatus 111 butmay be remotely located in the field and may communicate with network109.

The apparatus 111 may comprise a cab computer 115 that is programmedwith a cab application, which may comprise a version or variant of themobile application for device 104 that is further described in othersections herein. In an embodiment, cab computer 115 comprises a compactcomputer, often a tablet-sized computer or smartphone, with a graphicalscreen display, such as a color display, that is mounted within anoperator's cab of the apparatus 111. Cab computer 115 may implement someor all of the operations and functions that are described further hereinfor the mobile computer device 104.

The network(s) 109 broadly represent any combination of one or more datacommunication networks including local area networks, wide areanetworks, internetworks or internets, using any of wireline or wirelesslinks, including terrestrial or satellite links. The network(s) may beimplemented by any medium or mechanism that provides for the exchange ofdata between the various elements of FIG. 1 . The various elements ofFIG. 1 may also have direct (wired or wireless) communications links.The sensors 112, controller 114, external data server computer 108, andother elements of the system each comprise an interface compatible withthe network(s) 109 and are programmed or configured to use standardizedprotocols for communication across the networks such as TCP/IP,Bluetooth, CAN protocol and higher-layer protocols such as HTTP, TLS,and the like.

Agricultural intelligence computer system 130 is programmed orconfigured to receive field data 106 from field manager computing device104, external data 110 from external data server computer 108, andsensor data from remote sensor 112. Agricultural intelligence computersystem 130 may be further configured to host, use or execute one or morecomputer programs, other software elements, digitally programmed logicsuch as FPGAs or ASICs, or any combination thereof to performtranslation and storage of data values, construction of digital modelsof one or more crops on one or more fields, generation ofrecommendations and notifications, and generation and sending of scriptsto application controller 114, in the manner described further in othersections of this disclosure.

In an embodiment, agricultural intelligence computer system 130 isprogrammed with or comprises a communication layer 132, presentationlayer 134, data management layer 140, hardware/virtualization layer 150,and model and field data repository 160. “Layer,” in this context,refers to any combination of electronic digital interface circuits,microcontrollers, firmware such as drivers, and/or computer programs orother software elements.

Communication layer 132 may be programmed or configured to performinput/output interfacing functions including sending requests to fieldmanager computing device 104, external data server computer 108, andremote sensor 112 for field data, external data, and sensor datarespectively. Communication layer 132 may be programmed or configured tosend the received data to model and field data repository 160 to bestored as field data 106.

Presentation layer 134 may be programmed or configured to generate agraphical user interface (GUI) to be displayed on field managercomputing device 104, cab computer 115 or other computers that arecoupled to the system 130 through the network 109. The GUI may comprisecontrols for inputting data to be sent to agricultural intelligencecomputer system 130, generating requests for models and/orrecommendations, and/or displaying recommendations, notifications,models, and other field data.

Data management layer 140 may be programmed or configured to manage readoperations and write operations involving the repository 160 and otherfunctional elements of the system, including queries and result setscommunicated between the functional elements of the system and therepository. Examples of data management layer 140 include JDBC, SQLserver interface code, and/or HADOOP interface code, among others.Repository 160 may comprise a database. As used herein, the term“database” may refer to either a body of data, a relational databasemanagement system (RDBMS), or to both. As used herein, a database maycomprise any collection of data including hierarchical databases,relational databases, flat file databases, object-relational databases,object oriented databases, distributed databases, and any otherstructured collection of records or data that is stored in a computersystem. Examples of RDBMS's include, but are not limited to including,ORACLE®, MYSQL, IBM® DB2, MICROSOFT® SQL SERVER, SYBASE®, and POSTGRESQLdatabases. However, any database may be used that enables the systemsand methods described herein.

When field data 106 is not provided directly to the agriculturalintelligence computer system via one or more agricultural machines oragricultural machine devices that interacts with the agriculturalintelligence computer system, the user may be prompted via one or moreuser interfaces on the user device (served by the agriculturalintelligence computer system) to input such information. In an exampleembodiment, the user may specify identification data by accessing a mapon the user device (served by the agricultural intelligence computersystem) and selecting specific CLUs that have been graphically shown onthe map. In an alternative embodiment, the user 102 may specifyidentification data by accessing a map on the user device (served by theagricultural intelligence computer system 130) and drawing boundaries ofthe field over the map. Such CLU selection or map drawings representgeographic identifiers. In alternative embodiments, the user may specifyidentification data by accessing field identification data (provided asshape files or in a similar format) from the U. S. Department ofAgriculture Farm Service Agency or other source via the user device andproviding such field identification data to the agriculturalintelligence computer system.

In an example embodiment, the agricultural intelligence computer system130 is programmed to generate and cause displaying a graphical userinterface comprising a data manager for data input. After one or morefields have been identified using the methods described above, the datamanager may provide one or more graphical user interface widgets whichwhen selected can identify changes to the field, soil, crops, tillage,or nutrient practices. The data manager may include a timeline view, aspreadsheet view, and/or one or more editable programs.

FIG. 5 depicts an example embodiment of a timeline view for data entry.Using the display depicted in FIG. 5 , a user computer can input aselection of a particular field and a particular date for the additionof event. Events depicted at the top of the timeline may includeNitrogen, Planting, Practices, and Soil. To add a nitrogen applicationevent, a user computer may provide input to select the nitrogen tab. Theuser computer may then select a location on the timeline for aparticular field in order to indicate an application of nitrogen on theselected field. In response to receiving a selection of a location onthe timeline for a particular field, the data manager may display a dataentry overlay, allowing the user computer to input data pertaining tonitrogen applications, planting procedures, soil application, tillageprocedures, irrigation practices, or other information relating to theparticular field. For example, if a user computer selects a portion ofthe timeline and indicates an application of nitrogen, then the dataentry overlay may include fields for inputting an amount of nitrogenapplied, a date of application, a type of fertilizer used, and any otherinformation related to the application of nitrogen.

In an embodiment, the data manager provides an interface for creatingone or more programs. “Program,” in this context, refers to a set ofdata pertaining to nitrogen applications, planting procedures, soilapplication, tillage procedures, irrigation practices, or otherinformation that may be related to one or more fields, and that can bestored in digital data storage for reuse as a set in other operations.After a program has been created, it may be conceptually applied to oneor more fields and references to the program may be stored in digitalstorage in association with data identifying the fields. Thus, insteadof manually entering identical data relating to the same nitrogenapplications for multiple different fields, a user computer may create aprogram that indicates a particular application of nitrogen and thenapply the program to multiple different fields. For example, in thetimeline view of FIG. 5 , the top two timelines have the “Springapplied” program selected, which includes an application of 150 lbs N/acin early April. The data manager may provide an interface for editing aprogram. In an embodiment, when a particular program is edited, eachfield that has selected the particular program is edited. For example,in FIG. 5 , if the “Spring applied” program is edited to reduce theapplication of nitrogen to 130 lbs N/ac, the top two fields may beupdated with a reduced application of nitrogen based on the editedprogram.

In an embodiment, in response to receiving edits to a field that has aprogram selected, the data manager removes the correspondence of thefield to the selected program. For example, if a nitrogen application isadded to the top field in FIG. 5 , the interface may update to indicatethat the “Spring applied” program is no longer being applied to the topfield. While the nitrogen application in early April may remain, updatesto the “Spring applied” program would not alter the April application ofnitrogen.

FIG. 6 depicts an example embodiment of a spreadsheet view for dataentry. Using the display depicted in FIG. 6 , a user can create and editinformation for one or more fields. The data manager may includespreadsheets for inputting information with respect to Nitrogen,Planting, Practices, and Soil as depicted in FIG. 6 . To edit aparticular entry, a user computer may select the particular entry in thespreadsheet and update the values. For example, FIG. 6 depicts anin-progress update to a target yield value for the second field.Additionally, a user computer may select one or more fields in order toapply one or more programs. In response to receiving a selection of aprogram for a particular field, the data manager may automaticallycomplete the entries for the particular field based on the selectedprogram. As with the timeline view, the data manager may update theentries for each field associated with a particular program in responseto receiving an update to the program. Additionally, the data managermay remove the correspondence of the selected program to the field inresponse to receiving an edit to one of the entries for the field.

In an embodiment, model and field data is stored in model and field datarepository 160. Model data comprises data models created for one or morefields. For example, a crop model may include a digitally constructedmodel of the development of a crop on the one or more fields. “Model,”in this context, refers to an electronic digitally stored set ofexecutable instructions and data values, associated with one another,which are capable of receiving and responding to a programmatic or otherdigital call, invocation, or request for resolution based upon specifiedinput values, to yield one or more stored or calculated output valuesthat can serve as the basis of computer-implemented recommendations,output data displays, or machine control, among other things. Persons ofskill in the field find it convenient to express models usingmathematical equations, but that form of expression does not confine themodels disclosed herein to abstract concepts; instead, each model hereinhas a practical application in a computer in the form of storedexecutable instructions and data that implement the model using thecomputer. The model may include a model of past events on the one ormore fields, a model of the current status of the one or more fields,and/or a model of predicted events on the one or more fields. Model andfield data may be stored in data structures in memory, rows in adatabase table, in flat files or spreadsheets, or other forms of storeddigital data.

In an embodiment, each of cloud detecting machine learning system 136,agronomic modeling instructions 137, and agronomic map generationinstructions 138 comprises a set of one or more pages of main memory,such as RAM, in the agricultural intelligence computer system 130 intowhich executable instructions have been loaded and which when executedcause the agricultural intelligence computer system to perform thefunctions or operations that are described herein with reference tothose modules. For example, the agronomic modeling instructions 137 maycomprise a set of pages in RAM that contain instructions which whenexecuted cause performing the agronomic modeling functions that aredescribed herein. The instructions may be in machine executable code inthe instruction set of a CPU and may have been compiled based uponsource code written in JAVA, C, C++, OBJECTIVE-C, or any otherhuman-readable programming language or environment, alone or incombination with scripts in JAVASCRIPT, other scripting languages andother programming source text. The term “pages” is intended to referbroadly to any region within main memory and the specific terminologyused in a system may vary depending on the memory architecture orprocessor architecture. In another embodiment, each of cloud detectingmachine learning system 136, agronomic modeling instructions 137, andagronomic map generation instructions 138 also may represent one or morefiles or projects of source code that are digitally stored in a massstorage device such as non-volatile RAM or disk storage, in theagricultural intelligence computer system 130 or a separate repositorysystem, which when compiled or interpreted cause generating executableinstructions which when executed cause the agricultural intelligencecomputer system to perform the functions or operations that aredescribed herein with reference to those modules. In other words, thedrawing figure may represent the manner in which programmers or softwaredevelopers organize and arrange source code for later compilation intoan executable, or interpretation into bytecode or the equivalent, forexecution by the agricultural intelligence computer system 130.

Cloud detecting machine learning system 136 comprise computer readableinstructions which, when executed by one or more processors, causeagricultural intelligence computer system 130 to computationally detectcloud and/or cloud shadow pixels in satellite imagery. Agronomicmodeling instructions 137 comprise computer readable instructions which,when executed by one or more processors, cause agricultural intelligencecomputer system 130 to generate an agronomic model of an agronomic fieldbased, at least in part, on values derived from satellite imagery.Agronomic map generation instructions 138 comprise computer readableinstructions which, when executed by one or more processors, causeagricultural intelligence computer system 130 to generate an agronomicmap for display based, at least in part, on satellite imagery and dataidentifying pixels in the satellite imagery as cloud or cloud shadowpixels.

Hardware/virtualization layer 150 comprises one or more centralprocessing units (CPUs), memory controllers, and other devices,components, or elements of a computer system such as volatile ornon-volatile memory, non-volatile storage such as disk, and I/O devicesor interfaces as illustrated and described, for example, in connectionwith FIG. 4 . The layer 150 also may comprise programmed instructionsthat are configured to support virtualization, containerization, orother technologies.

For purposes of illustrating a clear example, FIG. 1 shows a limitednumber of instances of certain functional elements. However, in otherembodiments, there may be any number of such elements. For example,embodiments may use thousands or millions of different mobile computingdevices 104 associated with different users. Further, the system 130and/or external data server computer 108 may be implemented using two ormore processors, cores, clusters, or instances of physical machines orvirtual machines, configured in a discrete location or co-located withother elements in a datacenter, shared computing facility or cloudcomputing facility.

2.2. Application Program Overview

In an embodiment, the implementation of the functions described hereinusing one or more computer programs or other software elements that areloaded into and executed using one or more general-purpose computerswill cause the general-purpose computers to be configured as aparticular machine or as a computer that is specially adapted to performthe functions described herein. Further, each of the flow diagrams thatare described further herein may serve, alone or in combination with thedescriptions of processes and functions in prose herein, as algorithms,plans or directions that may be used to program a computer or logic toimplement the functions that are described. In other words, all theprose text herein, and all the drawing figures, together are intended toprovide disclosure of algorithms, plans or directions that aresufficient to permit a skilled person to program a computer to performthe functions that are described herein, in combination with the skilland knowledge of such a person given the level of skill that isappropriate for inventions and disclosures of this type.

In an embodiment, user 102 interacts with agricultural intelligencecomputer system 130 using field manager computing device 104 configuredwith an operating system and one or more application programs or apps;the field manager computing device 104 also may interoperate with theagricultural intelligence computer system independently andautomatically under program control or logical control and direct userinteraction is not always required. Field manager computing device 104broadly represents one or more of a smart phone, PDA, tablet computingdevice, laptop computer, desktop computer, workstation, or any othercomputing device capable of transmitting and receiving information andperforming the functions described herein. Field manager computingdevice 104 may communicate via a network using a mobile applicationstored on field manager computing device 104, and in some embodiments,the device may be coupled using a cable 113 or connector to the sensor112 and/or controller 114. A particular user 102 may own, operate orpossess and use, in connection with system 130, more than one fieldmanager computing device 104 at a time.

The mobile application may provide client-side functionality, via thenetwork to one or more mobile computing devices. In an exampleembodiment, field manager computing device 104 may access the mobileapplication via a web browser or a local client application or app.Field manager computing device 104 may transmit data to, and receivedata from, one or more front-end servers, using web-based protocols orformats such as HTTP, XML, and/or JSON, or app-specific protocols. In anexample embodiment, the data may take the form of requests and userinformation input, such as field data, into the mobile computing device.In some embodiments, the mobile application interacts with locationtracking hardware and software on field manager computing device 104which determines the location of field manager computing device 104using standard tracking techniques such as multilateration of radiosignals, the global positioning system (GPS), WiFi positioning systems,or other methods of mobile positioning. In some cases, location data orother data associated with the device 104, user 102, and/or useraccount(s) may be obtained by queries to an operating system of thedevice or by requesting an app on the device to obtain data from theoperating system.

In an embodiment, field manager computing device 104 sends field data106 to agricultural intelligence computer system 130 comprising orincluding, but not limited to, data values representing one or more of:a geographical location of the one or more fields, tillage informationfor the one or more fields, crops planted in the one or more fields, andsoil data extracted from the one or more fields. Field manager computingdevice 104 may send field data 106 in response to user input from user102 specifying the data values for the one or more fields. Additionally,field manager computing device 104 may automatically send field data 106when one or more of the data values becomes available to field managercomputing device 104. For example, field manager computing device 104may be communicatively coupled to remote sensor 112 and/or applicationcontroller 114 which include an irrigation sensor and/or irrigationcontroller. In response to receiving data indicating that applicationcontroller 114 released water onto the one or more fields, field managercomputing device 104 may send field data 106 to agriculturalintelligence computer system 130 indicating that water was released onthe one or more fields. Field data 106 identified in this disclosure maybe input and communicated using electronic digital data that iscommunicated between computing devices using parameterized URLs overHTTP, or another suitable communication or messaging protocol.

A commercial example of the mobile application is CLIMATE FIELDVIEW,commercially available from The Climate Corporation, San Francisco,Calif. The CLIMATE FIELDVIEW application, or other applications, may bemodified, extended, or adapted to include features, functions, andprogramming that have not been disclosed earlier than the filing date ofthis disclosure. In one embodiment, the mobile application comprises anintegrated software platform that allows a grower to make fact-baseddecisions for their operation because it combines historical data aboutthe grower's fields with any other data that the grower wishes tocompare. The combinations and comparisons may be performed in real timeand are based upon scientific models that provide potential scenarios topermit the grower to make better, more informed decisions.

FIG. 2 illustrates two views of an example logical organization of setsof instructions in main memory when an example mobile application isloaded for execution. In FIG. 2 , each named element represents a regionof one or more pages of RAM or other main memory, or one or more blocksof disk storage or other non-volatile storage, and the programmedinstructions within those regions. In one embodiment, in view (a), amobile computer application 200 comprises account-fields-dataingestion-sharing instructions 202, overview and alert instructions 204,digital map book instructions 206, seeds and planting instructions 208,nitrogen instructions 210, weather instructions 212, field healthinstructions 214, and performance instructions 216.

In one embodiment, a mobile computer application 200 comprises account,fields, data ingestion, sharing instructions 202 which are programmed toreceive, translate, and ingest field data from third party systems viamanual upload or APIs. Data types may include field boundaries, yieldmaps, as-planted maps, soil test results, as-applied maps, and/ormanagement zones, among others. Data formats may include shape files,native data formats of third parties, and/or farm management informationsystem (FMIS) exports, among others. Receiving data may occur via manualupload, e-mail with attachment, external APIs that push data to themobile application, or instructions that call APIs of external systemsto pull data into the mobile application. In one embodiment, mobilecomputer application 200 comprises a data inbox. In response toreceiving a selection of the data inbox, the mobile computer application200 may display a graphical user interface for manually uploading datafiles and importing uploaded files to a data manager.

In one embodiment, digital map book instructions 206 comprise field mapdata layers stored in device memory and are programmed with datavisualization tools and geospatial field notes. This provides growerswith convenient information close at hand for reference, logging andvisual insights into field performance. In one embodiment, overview andalert instructions 204 are programmed to provide an operation-wide viewof what is important to the grower, and timely recommendations to takeaction or focus on particular issues. This permits the grower to focustime on what needs attention, to save time and preserve yield throughoutthe season. In one embodiment, seeds and planting instructions 208 areprogrammed to provide tools for seed selection, hybrid placement, andscript creation, including variable rate (VR) script creation, basedupon scientific models and empirical data. This enables growers tomaximize yield or return on investment through optimized seed purchase,placement and population.

In one embodiment, script generation instructions 205 are programmed toprovide an interface for generating scripts, including variable rate(VR) fertility scripts. The interface enables growers to create scriptsfor field implements, such as nutrient applications, planting, andirrigation. For example, a planting script interface may comprise toolsfor identifying a type of seed for planting. Upon receiving a selectionof the seed type, mobile computer application 200 may display one ormore fields broken into management zones, such as the field map datalayers created as part of digital map book instructions 206. In oneembodiment, the management zones comprise soil zones along with a panelidentifying each soil zone and a soil name, texture, drainage for eachzone, or other field data. Mobile computer application 200 may alsodisplay tools for editing or creating such, such as graphical tools fordrawing management zones, such as soil zones, over a map of one or morefields. Planting procedures may be applied to all management zones ordifferent planting procedures may be applied to different subsets ofmanagement zones. When a script is created, mobile computer application200 may make the script available for download in a format readable byan application controller, such as an archived or compressed format.Additionally, and/or alternatively, a script may be sent directly to cabcomputer 115 from mobile computer application 200 and/or uploaded to oneor more data servers and stored for further use.

In one embodiment, nitrogen instructions 210 are programmed to providetools to inform nitrogen decisions by visualizing the availability ofnitrogen to crops. This enables growers to maximize yield or return oninvestment through optimized nitrogen application during the season.Example programmed functions include displaying images such as SSURGOimages to enable drawing of fertilizer application zones and/or imagesgenerated from subfield soil data, such as data obtained from sensors,at a high spatial resolution (as fine as millimeters or smallerdepending on sensor proximity and resolution); upload of existinggrower-defined zones; providing a graph of plant nutrient availabilityand/or a map to enable tuning application(s) of nitrogen across multiplezones; output of scripts to drive machinery; tools for mass data entryand adjustment; and/or maps for data visualization, among others. “Massdata entry,” in this context, may mean entering data once and thenapplying the same data to multiple fields and/or zones that have beendefined in the system; example data may include nitrogen applicationdata that is the same for many fields and/or zones of the same grower,but such mass data entry applies to the entry of any type of field datainto the mobile computer application 200. For example, nitrogeninstructions 210 may be programmed to accept definitions of nitrogenapplication and practices programs and to accept user input specifyingto apply those programs across multiple fields. “Nitrogen applicationprograms,” in this context, refers to stored, named sets of data thatassociates: a name, color code or other identifier, one or more dates ofapplication, types of material or product for each of the dates andamounts, method of application or incorporation such as injected orbroadcast, and/or amounts or rates of application for each of the dates,crop or hybrid that is the subject of the application, among others.“Nitrogen practices programs,” in this context, refer to stored, namedsets of data that associates: a practices name; a previous crop; atillage system; a date of primarily tillage; one or more previoustillage systems that were used; one or more indicators of applicationtype, such as manure, that were used. Nitrogen instructions 210 also maybe programmed to generate and cause displaying a nitrogen graph, whichindicates projections of plant use of the specified nitrogen and whethera surplus or shortfall is predicted; in some embodiments, differentcolor indicators may signal a magnitude of surplus or magnitude ofshortfall. In one embodiment, a nitrogen graph comprises a graphicaldisplay in a computer display device comprising a plurality of rows,each row associated with and identifying a field; data specifying whatcrop is planted in the field, the field size, the field location, and agraphic representation of the field perimeter; in each row, a timelineby month with graphic indicators specifying each nitrogen applicationand amount at points correlated to month names; and numeric and/orcolored indicators of surplus or shortfall, in which color indicatesmagnitude.

In one embodiment, the nitrogen graph may include one or more user inputfeatures, such as dials or slider bars, to dynamically change thenitrogen planting and practices programs so that a user may optimize hisnitrogen graph. The user may then use his optimized nitrogen graph andthe related nitrogen planting and practices programs to implement one ormore scripts, including variable rate (VR) fertility scripts. Nitrogeninstructions 210 also may be programmed to generate and cause displayinga nitrogen map, which indicates projections of plant use of thespecified nitrogen and whether a surplus or shortfall is predicted; insome embodiments, different color indicators may signal a magnitude ofsurplus or magnitude of shortfall. The nitrogen map may displayprojections of plant use of the specified nitrogen and whether a surplusor shortfall is predicted for different times in the past and the future(such as daily, weekly, monthly or yearly) using numeric and/or coloredindicators of surplus or shortfall, in which color indicates magnitude.In one embodiment, the nitrogen map may include one or more user inputfeatures, such as dials or slider bars, to dynamically change thenitrogen planting and practices programs so that a user may optimize hisnitrogen map, such as to obtain a preferred amount of surplus toshortfall. The user may then use his optimized nitrogen map and therelated nitrogen planting and practices programs to implement one ormore scripts, including variable rate (VR) fertility scripts. In otherembodiments, similar instructions to the nitrogen instructions 210 couldbe used for application of other nutrients (such as phosphorus andpotassium), application of pesticide, and irrigation programs.

In one embodiment, weather instructions 212 are programmed to providefield-specific recent weather data and forecasted weather information.This enables growers to save time and have an efficient integrateddisplay with respect to daily operational decisions.

In one embodiment, field health instructions 214 are programmed toprovide timely remote sensing images highlighting in-season cropvariation and potential concerns. Example programmed functions includecloud checking, to identify possible clouds or cloud shadows;determining nitrogen indices based on field images; graphicalvisualization of scouting layers, including, for example, those relatedto field health, and viewing and/or sharing of scouting notes; and/ordownloading satellite images from multiple sources and prioritizing theimages for the grower, among others.

In one embodiment, performance instructions 216 are programmed toprovide reports, analysis, and insight tools using on-farm data forevaluation, insights and decisions. This enables the grower to seekimproved outcomes for the next year through fact-based conclusions aboutwhy return on investment was at prior levels, and insight intoyield-limiting factors. The performance instructions 216 may beprogrammed to communicate via the network(s) 109 to back-end analyticsprograms executed at agricultural intelligence computer system 130and/or external data server computer 108 and configured to analyzemetrics such as yield, yield differential, hybrid, population, SSURGOzone, soil test properties, or elevation, among others. Programmedreports and analysis may include yield variability analysis, treatmenteffect estimation, benchmarking of yield and other metrics against othergrowers based on anonymized data collected from many growers, or datafor seeds and planting, among others.

Applications having instructions configured in this way may beimplemented for different computing device platforms while retaining thesame general user interface appearance. For example, the mobileapplication may be programmed for execution on tablets, smartphones, orserver computers that are accessed using browsers at client computers.Further, the mobile application as configured for tablet computers orsmartphones may provide a full app experience or a cab app experiencethat is suitable for the display and processing capabilities of cabcomputer 115. For example, referring now to view (b) of FIG. 2 , in oneembodiment a cab computer application 220 may comprise maps-cabinstructions 222, remote view instructions 224, data collect andtransfer instructions 226, machine alerts instructions 228, scripttransfer instructions 230, and scouting-cab instructions 232. The codebase for the instructions of view (b) may be the same as for view (a)and executables implementing the code may be programmed to detect thetype of platform on which they are executing and to expose, through agraphical user interface, only those functions that are appropriate to acab platform or full platform. This approach enables the system torecognize the distinctly different user experience that is appropriatefor an in-cab environment and the different technology environment ofthe cab. The maps-cab instructions 222 may be programmed to provide mapviews of fields, farms or regions that are useful in directing machineoperation. The remote view instructions 224 may be programmed to turnon, manage, and provide views of machine activity in real-time or nearreal-time to other computing devices connected to the system 130 viawireless networks, wired connectors or adapters, and the like. The datacollect and transfer instructions 226 may be programmed to turn on,manage, and provide transfer of data collected at sensors andcontrollers to the system 130 via wireless networks, wired connectors oradapters, and the like. The machine alerts instructions 228 may beprogrammed to detect issues with operations of the machine or tools thatare associated with the cab and generate operator alerts. The scripttransfer instructions 230 may be configured to transfer in scripts ofinstructions that are configured to direct machine operations or thecollection of data. The scouting-cab instructions 232 may be programmedto display location-based alerts and information received from thesystem 130 based on the location of the field manager computing device104, agricultural apparatus 111, or sensors 112 in the field and ingest,manage, and provide transfer of location-based scouting observations tothe system 130 based on the location of the agricultural apparatus 111or sensors 112 in the field.

2.3. Data Ingest to the Computer System

In an embodiment, external data server computer 108 stores external data110, including soil data representing soil composition for the one ormore fields and weather data representing temperature and precipitationon the one or more fields. The weather data may include past and presentweather data as well as forecasts for future weather data. In anembodiment, external data server computer 108 comprises a plurality ofservers hosted by different entities. For example, a first server maycontain soil composition data while a second server may include weatherdata. Additionally, soil composition data may be stored in multipleservers. For example, one server may store data representing percentageof sand, silt, and clay in the soil while a second server may store datarepresenting percentage of organic matter (OM) in the soil.

In an embodiment, remote sensor 112 comprises one or more sensors thatare programmed or configured to produce one or more observations. Remotesensor 112 may be aerial sensors, such as satellites, vehicle sensors,planting equipment sensors, tillage sensors, fertilizer or insecticideapplication sensors, harvester sensors, and any other implement capableof receiving data from the one or more fields. In an embodiment,application controller 114 is programmed or configured to receiveinstructions from agricultural intelligence computer system 130.Application controller 114 may also be programmed or configured tocontrol an operating parameter of an agricultural vehicle or implement.For example, an application controller may be programmed or configuredto control an operating parameter of a vehicle, such as a tractor,planting equipment, tillage equipment, fertilizer or insecticideequipment, harvester equipment, or other farm implements such as a watervalve. Other embodiments may use any combination of sensors andcontrollers, of which the following are merely selected examples.

The system 130 may obtain or ingest data under user 102 control, on amass basis from a large number of growers who have contributed data to ashared database system. This form of obtaining data may be termed“manual data ingest” as one or more user-controlled computer operationsare requested or triggered to obtain data for use by the system 130. Asan example, the CLIMATE FIELDVIEW application, commercially availablefrom The Climate Corporation, San Francisco, Calif., may be operated toexport data to system 130 for storing in the repository 160.

For example, seed monitor systems can both control planter apparatuscomponents and obtain planting data, including signals from seed sensorsvia a signal harness that comprises a CAN backbone and point-to-pointconnections for registration and/or diagnostics. Seed monitor systemscan be programmed or configured to display seed spacing, population andother information to the user via the cab computer 115 or other deviceswithin the system 130. Examples are disclosed in U.S. Pat. No. 8,738,243and US Pat. Pub. 20150094916, and the present disclosure assumesknowledge of those other patent disclosures.

Likewise, yield monitor systems may contain yield sensors for harvesterapparatus that send yield measurement data to the cab computer 115 orother devices within the system 130. Yield monitor systems may utilizeone or more remote sensors 112 to obtain grain moisture measurements ina combine or other harvester and transmit these measurements to the uservia the cab computer 115 or other devices within the system 130.

In an embodiment, examples of sensors 112 that may be used with anymoving vehicle or apparatus of the type described elsewhere hereininclude kinematic sensors and position sensors. Kinematic sensors maycomprise any of speed sensors such as radar or wheel speed sensors,accelerometers, or gyros. Position sensors may comprise GPS receivers ortransceivers, or WiFi-based position or mapping apps that are programmedto determine location based upon nearby WiFi hotspots, among others.

In an embodiment, examples of sensors 112 that may be used with tractorsor other moving vehicles include engine speed sensors, fuel consumptionsensors, area counters or distance counters that interact with GPS orradar signals, PTO (power take-off) speed sensors, tractor hydraulicssensors configured to detect hydraulics parameters such as pressure orflow, and/or and hydraulic pump speed, wheel speed sensors or wheelslippage sensors. In an embodiment, examples of controllers 114 that maybe used with tractors include hydraulic directional controllers,pressure controllers, and/or flow controllers; hydraulic pump speedcontrollers; speed controllers or governors; hitch position controllers;or wheel position controllers provide automatic steering.

In an embodiment, examples of sensors 112 that may be used with seedplanting equipment such as planters, drills, or air seeders include seedsensors, which may be optical, electromagnetic, or impact sensors;downforce sensors such as load pins, load cells, pressure sensors; soilproperty sensors such as reflectivity sensors, moisture sensors,electrical conductivity sensors, optical residue sensors, or temperaturesensors; component operating criteria sensors such as planting depthsensors, downforce cylinder pressure sensors, seed disc speed sensors,seed drive motor encoders, seed conveyor system speed sensors, or vacuumlevel sensors; or pesticide application sensors such as optical or otherelectromagnetic sensors, or impact sensors. In an embodiment, examplesof controllers 114 that may be used with such seed planting equipmentinclude: toolbar fold controllers, such as controllers for valvesassociated with hydraulic cylinders; downforce controllers, such ascontrollers for valves associated with pneumatic cylinders, airbags, orhydraulic cylinders, and programmed for applying downforce to individualrow units or an entire planter frame; planting depth controllers, suchas linear actuators; metering controllers, such as electric seed meterdrive motors, hydraulic seed meter drive motors, or swath controlclutches; hybrid selection controllers, such as seed meter drive motors,or other actuators programmed for selectively allowing or preventingseed or an air-seed mixture from delivering seed to or from seed metersor central bulk hoppers; metering controllers, such as electric seedmeter drive motors, or hydraulic seed meter drive motors; seed conveyorsystem controllers, such as controllers for a belt seed deliveryconveyor motor; marker controllers, such as a controller for a pneumaticor hydraulic actuator; or pesticide application rate controllers, suchas metering drive controllers, orifice size or position controllers.

In an embodiment, examples of sensors 112 that may be used with tillageequipment include position sensors for tools such as shanks or discs;tool position sensors for such tools that are configured to detectdepth, gang angle, or lateral spacing; downforce sensors; or draft forcesensors. In an embodiment, examples of controllers 114 that may be usedwith tillage equipment include downforce controllers or tool positioncontrollers, such as controllers configured to control tool depth, gangangle, or lateral spacing.

In an embodiment, examples of sensors 112 that may be used in relationto apparatus for applying fertilizer, insecticide, fungicide and thelike, such as on-planter starter fertilizer systems, subsoil fertilizerapplicators, or fertilizer sprayers, include: fluid system criteriasensors, such as flow sensors or pressure sensors; sensors indicatingwhich spray head valves or fluid line valves are open; sensorsassociated with tanks, such as fill level sensors; sectional orsystem-wide supply line sensors, or row-specific supply line sensors; orkinematic sensors such as accelerometers disposed on sprayer booms. Inan embodiment, examples of controllers 114 that may be used with suchapparatus include pump speed controllers; valve controllers that areprogrammed to control pressure, flow, direction, PWM and the like; orposition actuators, such as for boom height, subsoiler depth, or boomposition.

In an embodiment, examples of sensors 112 that may be used withharvesters include yield monitors, such as impact plate strain gauges orposition sensors, capacitive flow sensors, load sensors, weight sensors,or torque sensors associated with elevators or augers, or optical orother electromagnetic grain height sensors; grain moisture sensors, suchas capacitive sensors; grain loss sensors, including impact, optical, orcapacitive sensors; header operating criteria sensors such as headerheight, header type, deck plate gap, feeder speed, and reel speedsensors; separator operating criteria sensors, such as concaveclearance, rotor speed, shoe clearance, or chaffer clearance sensors;auger sensors for position, operation, or speed; or engine speedsensors. In an embodiment, examples of controllers 114 that may be usedwith harvesters include header operating criteria controllers forelements such as header height, header type, deck plate gap, feederspeed, or reel speed; separator operating criteria controllers forfeatures such as concave clearance, rotor speed, shoe clearance, orchaffer clearance; or controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 that may be used with graincarts include weight sensors, or sensors for auger position, operation,or speed. In an embodiment, examples of controllers 114 that may be usedwith grain carts include controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 and controllers 114 may beinstalled in unmanned aerial vehicle (UAV) apparatus or “drones.” Suchsensors may include cameras with detectors effective for any range ofthe electromagnetic spectrum including visible light, infrared,ultraviolet, near-infrared (NIR), and the like; accelerometers;altimeters; temperature sensors; humidity sensors; pitot tube sensors orother airspeed or wind velocity sensors; battery life sensors; or radaremitters and reflected radar energy detection apparatus; otherelectromagnetic radiation emitters and reflected electromagneticradiation detection apparatus. Such controllers may include guidance ormotor control apparatus, control surface controllers, cameracontrollers, or controllers programmed to turn on, operate, obtain datafrom, manage and configure any of the foregoing sensors. Examples aredisclosed in U.S. patent application Ser. No. 14/831,165 and the presentdisclosure assumes knowledge of that other patent disclosure.

In an embodiment, sensors 112 and controllers 114 may be affixed to soilsampling and measurement apparatus that is configured or programmed tosample soil and perform soil chemistry tests, soil moisture tests, andother tests pertaining to soil. For example, the apparatus disclosed inU.S. Pat. Nos. 8,767,194 and 8,712,148 may be used, and the presentdisclosure assumes knowledge of those patent disclosures.

In an embodiment, sensors 112 and controllers 114 may comprise weatherdevices for monitoring weather conditions of fields. For example, theapparatus disclosed in U.S. Provisional Application No. 62/154,207,filed on Apr. 29, 2015, U.S. Provisional Application No. 62/175,160,filed on Jun. 12, 2015, U.S. Provisional Application No. 62/198,060,filed on Jul. 28, 2015, and U.S. Provisional Application No. 62/220,852,filed on Sep. 18, 2015, may be used, and the present disclosure assumesknowledge of those patent disclosures.

2.4. Process Overview-Agronomic Model Training

In an embodiment, the agricultural intelligence computer system 130 isprogrammed or configured to create an agronomic model. In this context,an agronomic model is a data structure in memory of the agriculturalintelligence computer system 130 that comprises field data 106, such asidentification data and harvest data for one or more fields. Theagronomic model may also comprise calculated agronomic properties whichdescribe either conditions which may affect the growth of one or morecrops on a field, or properties of the one or more crops, or both.Additionally, an agronomic model may comprise recommendations based onagronomic factors such as crop recommendations, irrigationrecommendations, planting recommendations, fertilizer recommendations,fungicide recommendations, pesticide recommendations, harvestingrecommendations and other crop management recommendations. The agronomicfactors may also be used to estimate one or more crop related results,such as agronomic yield. The agronomic yield of a crop is an estimate ofquantity of the crop that is produced, or in some examples the revenueor profit obtained from the produced crop.

In an embodiment, the agricultural intelligence computer system 130 mayuse a preconfigured agronomic model to calculate agronomic propertiesrelated to currently received location and crop information for one ormore fields. The preconfigured agronomic model is based upon previouslyprocessed field data, including but not limited to, identification data,harvest data, fertilizer data, and weather data. The preconfiguredagronomic model may have been cross validated to ensure accuracy of themodel. Cross validation may include comparison to ground truthing thatcompares predicted results with actual results on a field, such as acomparison of precipitation estimate with a rain gauge or sensorproviding weather data at the same or nearby location or an estimate ofnitrogen content with a soil sample measurement.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using field data provided by one or more data sources.FIG. 3 may serve as an algorithm or instructions for programming thefunctional elements of the agricultural intelligence computer system 130to perform the operations that are now described.

At block 305, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic data preprocessing offield data received from one or more data sources. The field datareceived from one or more data sources may be preprocessed for thepurpose of removing noise, distorting effects, and confounding factorswithin the agronomic data including measured outliers that couldadversely affect received field data values. Embodiments of agronomicdata preprocessing may include, but are not limited to, removing datavalues commonly associated with outlier data values, specific measureddata points that are known to unnecessarily skew other data values, datasmoothing, aggregation, or sampling techniques used to remove or reduceadditive or multiplicative effects from noise, and other filtering ordata derivation techniques used to provide clear distinctions betweenpositive and negative data inputs.

At block 310, the agricultural intelligence computer system 130 isconfigured or programmed to perform data subset selection using thepreprocessed field data in order to identify datasets useful for initialagronomic model generation. The agricultural intelligence computersystem 130 may implement data subset selection techniques including, butnot limited to, a genetic algorithm method, an all subset models method,a sequential search method, a stepwise regression method, a particleswarm optimization method, and an ant colony optimization method. Forexample, a genetic algorithm selection technique uses an adaptiveheuristic search algorithm, based on evolutionary principles of naturalselection and genetics, to determine and evaluate datasets within thepreprocessed agronomic data.

At block 315, the agricultural intelligence computer system 130 isconfigured or programmed to implement field dataset evaluation. In anembodiment, a specific field dataset is evaluated by creating anagronomic model and using specific quality thresholds for the createdagronomic model. Agronomic models may be compared and/or validated usingone or more comparison techniques, such as, but not limited to, rootmean square error with leave-one-out cross validation (RMSECV), meanabsolute error, and mean percentage error. For example, RMSECV can crossvalidate agronomic models by comparing predicted agronomic propertyvalues created by the agronomic model against historical agronomicproperty values collected and analyzed. In an embodiment, the agronomicdataset evaluation logic is used as a feedback loop where agronomicdatasets that do not meet configured quality thresholds are used duringfuture data subset selection steps (block 310).

At block 320, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic model creation basedupon the cross validated agronomic datasets. In an embodiment, agronomicmodel creation may implement multivariate regression techniques tocreate preconfigured agronomic data models.

At block 325, the agricultural intelligence computer system 130 isconfigured or programmed to store the preconfigured agronomic datamodels for future field data evaluation.

2.5. Implementation Example—Hardware Overview

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs) or fieldprogrammable gate arrays (FPGAs) that are persistently programmed toperform the techniques, or may include one or more general purposehardware processors programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. Such special-purpose computing devices may also combinecustom hard-wired logic, ASICs, or FPGAs with custom programming toaccomplish the techniques. The special-purpose computing devices may bedesktop computer systems, portable computer systems, handheld devices,networking devices or any other device that incorporates hard-wiredand/or program logic to implement the techniques.

For example, FIG. 4 is a block diagram that illustrates a computersystem 400 upon which an embodiment of the invention may be implemented.Computer system 400 includes a bus 402 or other communication mechanismfor communicating information, and a hardware processor 404 coupled withbus 402 for processing information. Hardware processor 404 may be, forexample, a general purpose microprocessor.

Computer system 400 also includes a main memory 406, such as a randomaccess memory (RAM) or other dynamic storage device, coupled to bus 402for storing information and instructions to be executed by processor404. Main memory 406 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 404. Such instructions, when stored innon-transitory storage media accessible to processor 404, rendercomputer system 400 into a special-purpose machine that is customized toperform the operations specified in the instructions.

Computer system 400 further includes a read only memory (ROM) 408 orother static storage device coupled to bus 402 for storing staticinformation and instructions for processor 404. A storage device 410,such as a magnetic disk, optical disk, or solid-state drive is providedand coupled to bus 402 for storing information and instructions.

Computer system 400 may be coupled via bus 402 to a display 412, such asa cathode ray tube (CRT), for displaying information to a computer user.An input device 414, including alphanumeric and other keys, is coupledto bus 402 for communicating information and command selections toprocessor 404. Another type of user input device is cursor control 416,such as a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to processor 404 and forcontrolling cursor movement on display 412. This input device typicallyhas two degrees of freedom in two axes, a first axis (e.g., x) and asecond axis (e.g., y), that allows the device to specify positions in aplane.

Computer system 400 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 400 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 400 in response to processor 404 executing one or more sequencesof one or more instructions contained in main memory 406. Suchinstructions may be read into main memory 406 from another storagemedium, such as storage device 410. Execution of the sequences ofinstructions contained in main memory 406 causes processor 404 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical disks, magnetic disks, or solid-state drives, suchas storage device 410. Volatile media includes dynamic memory, such asmain memory 406. Common forms of storage media include, for example, afloppy disk, a flexible disk, hard disk, solid-state drive, magnetictape, or any other magnetic data storage medium, a CD-ROM, any otheroptical data storage medium, any physical medium with patterns of holes,a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 402. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infrared data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 404 for execution. For example,the instructions may initially be carried on a magnetic disk orsolid-state drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 400 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infrared signal and appropriatecircuitry can place the data on bus 402. Bus 402 carries the data tomain memory 406, from which processor 404 retrieves and executes theinstructions. The instructions received by main memory 406 mayoptionally be stored on storage device 410 either before or afterexecution by processor 404.

Computer system 400 also includes a communication interface 418 coupledto bus 402. Communication interface 418 provides a two-way datacommunication coupling to a network link 420 that is connected to alocal network 422. For example, communication interface 418 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 418 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 418sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

Network link 420 typically provides data communication through one ormore networks to other data devices. For example, network link 420 mayprovide a connection through local network 422 to a host computer 424 orto data equipment operated by an Internet Service Provider (ISP) 426.ISP 426 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 428. Local network 422 and Internet 428 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 420and through communication interface 418, which carry the digital data toand from computer system 400, are example forms of transmission media.

Computer system 400 can send messages and receive data, includingprogram code, through the network(s), network link 420 and communicationinterface 418. In the Internet example, a server 430 might transmit arequested code for an application program through Internet 428, ISP 426,local network 422 and communication interface 418.

The received code may be executed by processor 404 as it is received,and/or stored in storage device 410, or other non-volatile storage forlater execution.

3. Cloud and Cloud Shadow Detection System

FIG. 7 depicts an example method for using a machine learning system toidentify pixels in an image as cloud locations.

3.1. Received Data

At step 702, images of agronomic fields produced using one or morefrequency bands are received. The images of the agronomic field may beproduced by a satellite configured to capture images in a plurality offrequency bands. For example, the SENTINEL-2 satellite operated by theEUROPEAN SPACE AGENCY produces images in a plurality of frequency bandsincluding a red frequency band, a blue frequency band, a green frequencyband, a near infrared frequency band, and a water vapor frequency band.

The agricultural intelligence computer system may receive a plurality ofsets of images directly or indirectly from the satellite where each setof images comprises an image in each frequency band corresponding to asame location. The system may receive any number of the above describedfrequency bands and/or different frequency bands for use in detectingcloud and cloud shadow in an image. The data may be received as a seriesof pixel values for images of each frequency band, the pixel valuescorresponding to pixel locations. In an embodiment, each image in a setof images is of a same size with pixels corresponding to overlappinglocations. For instance, the Nth pixel of the red frequency band imageof an agronomic field may correspond to the Nth pixel of the bluefrequency band image of the agronomic field in the same set as the redfrequency band image.

At step 704, corresponding data identifying cloud and cloud shadowlocations in the images is received. The data may indicate, for eachpixel of the images in a set of images, whether the pixel is a non-cloudpixel, a cloud pixel, or a cloud shadow pixel. For example, a set ofimages may include three frequency band images, each of which comprising300×300 pixels with the Nth pixel of each image corresponding to thesame location on the agronomic field. The corresponding data may thusindicate, for each pixel of the 300×300 pixel images, whether the pixelis a non-cloud pixel, a cloud pixel, or a cloud shadow pixel. Thus, ifthe system receives 300×300 pixel images of an agronomic field in threefrequency bands, the system may store three 300×300 matrices, each ofwhich corresponding to a frequency band, and a fourth 300×300 matrixwhere each element of the matrix is one of three values, such as 0, 1,and 2, which correspond to non-cloud pixels, cloud pixels, or cloudshadow pixels.

While embodiments are described where received data indicates both cloudand cloud shadow in a single mapping, in embodiments where a machinelearning system is only used to identify cloud locations, the system maystore a matrix indicating only the locations of cloud and non-cloudpixels where the non-cloud pixels include cloud-shadow pixels. Forembodiments where a second machine learning system is used to identifycloud shadow locations, the system may store a matrix indicating onlythe locations of cloud-shadow and non-cloud shadow pixels, therebyallowing the system to identify clouds and cloud shadows separatelyusing the methods described further herein.

3.2. Cloud Identifying Machine Learning System

At step 706, the system trains a machine learning system to identify atleast cloud locations using the images as inputs and at least dataidentifying cloud pixels and non-cloud pixels as outputs. The machinelearning system may comprise any machine learning system which iscapable of accepting one or more matrices of values as inputs andgenerating one or more matrices of values as outputs, such as aconvolutional neural network.

In an embodiment, the machine learning system is a convolutionalencoder-decoder for pixel-wise classification. For example, the machinelearning system may be the SegNet convolutional encoder-decoderarchitecture available on GITHUB. In order to capture relationshipsbetween adjacent pixels, the machine learning system may include aplurality of convolutional encoding steps interspersed with poolingsteps and a plurality of convolutional decoding steps interspersed withupsampling steps. In one embodiment, the SegNet convolutionalencoder-decoder is configured using a stochastic gradient descentoptimizer, a learning rate of 0.001, a decay of 1e-6, a momentum of 0.9,and Nesterov set to True.

As used herein, a pooling step comprises a step of reducing a spatialsize of a representation. For example, a maxpooling step may comprise astep of reducing a size of a matrix by selecting a maximum value in each2×2 portion of the matrix. As used herein, an upsampling step comprisesa step of increasing a spatial size of a representation, such as byinserting values into the matrix or duplicating values in the matrix. Asused herein, a softmax layer comprises a layer where a softmax functionis applied to the dataset, the softmax function taking an input of realnumbers and normalizing the input into a probability distribution.

FIG. 8 depicts an example convolutional encoder-decoder machine learningsystem. Machine learning system 800 includes encoder 802 and decoder808. Encoder 802 includes convolutional blocks 804 followed by poolingsteps 806. In the example machine learning system 800, the convolutionalblocks 804 of encoder 802 include increasing number of 3×3 filters, withthe first convolutional block comprising 64 3×3 filters and the finalconvolution block comprising 512 3×3 filters. The convolutional blocksare used to produce a set of feature maps. Pooling steps 806 areperformed to achieve translation invariance over small spatial shifts.

Decoder 808 includes convolutional blocks 810 and upsampling steps 812.The decoder performs upsampling steps 812 to upsample feature input mapsusing memorized maxpooling indices from the corresponding pooling step,with the final pooling step corresponding to the initial upsamplingstep. Convolutional blocks 810 are also a mirror of convolutional blocks804, with the first including 512 3×3 filters and the final blockincluding 64 3×3 filters, thereby ensuring the output is of the samesize and resolution as the input. In an embodiment, a convolutionalblock including two classes (cloud and non-cloud) of 1×1 filters areapplied after the decoder. A softmax layer may be added as the finalclassification layer to select the highest probability output.

In an embodiment, the system uses a machine learning system which iscapable of being trained on images of varying sizes and is capable ofproducing outputs with images of different sizes. The system may utilizea machine learning system which has been modified to accept inputs ofvarying sizes. For example, the SegNet convolutional encoder-decoder istraditionally fit with an image with an input size of 360×480 and abuilt in reshape function is used to reshape the output vector of theSegNet convolutional encoder-decoder into an image map vector duringtraining. To allow the SegNet convolutional encoder-decoder to work withimages of varying sizes, the input size may be specified as (None,None). A customized machine learning layer may then reshape the build ofthe vector based on a symbolic shape of the output tensor, therebyallowing the model to accept images of varying sizes as long as the sizeof each input image matches the size of its corresponding output imageduring training.

While the above method allows the machine learning system to utilizeimages of varying sizes, some image sizes may create issues during thepooling steps due to odd numbers of pixels. For example, FIG. 8 includesthree maxpooling steps which effectively reduce the size of the image bya factor of eight. Thus, the system may be configured to ensure that theinputs and corresponding outputs include a multiple of eight pixels forboth the width and height. For example, if an image has 365×484 pixels,the system may add 3 rows of pixels and 4 columns of pixels, each with apixel value of 0, to the input image and the output matrix, therebyensuring that the training of the machine learning system does notimproperly round pixel values. Additionally, inputs into the machinelearning system may be likewise modified to produce an output image ofthe same size. After the output is created, the machine learning systemmay remove any added rows or columns. Additionally or alternatively, thesystem may truncate the inputs and corresponding outputs to ensure thatthe inputs and corresponding outputs include a multiple of eight pixelsfor both the width and height, such as by removing columns and/or rowsfor training.

In an embodiment, the machine learning system is trained using aplurality of stacked matrices as inputs and a single matrix identifyinglocations of clouds as outputs. For example, if the machine learningsystem is trained on three frequency bands, each input/output pairingwould include three stacked matrices as inputs and a single cloudidentifying matrix as an output. Each of the three input matrices forthe input/output pairing may correspond to a different frequency bandand comprise pixel values for that frequency band. An example of theoutput single cloud identifying matrix is a matrix of 0s and is where a0 indicates that the pixel does not correspond to a cloud location and a1 indicates that the pixel corresponds to a cloud location.

In an embodiment, a single machine learning system is trained toidentify both clouds and cloud shadows in images. The machine learningsystem may be configured to separately identify clouds and cloudshadows. For example, instead of the output matrices including only 0sand 1s, the output matrices may include 0s, 1s, and 2s where a 0indicates that the pixel does not correspond to a cloud location, a 1indicates that the pixel corresponds to a cloud location, and a 2indicates that the pixel corresponds to a cloud shadow location. Whileboth clouds and cloud shadow pixels are identified for the purpose ofremoval and/or replacement, the system is able to more efficientlydistinguish between clouds/cloud shadows and pixels that are neithercloud nor cloud shadow by training the machine learning system withthree outputs instead of two.

Referring again to FIG. 7 , at step 708, one or more particular imagesof a particular agronomic field produced using the one or more frequencybands are received. The agricultural intelligence computer system mayreceive a set of images directly or indirectly from a satellite wherethe set of images comprises an image in each frequency bandcorresponding to the same location. In an embodiment, the received setof images includes an image in each frequency band used to train themachine learning system. For example, if the machine learning system wastrained using red, blue, and green frequency bands, the received set ofimages corresponding to the same location may include at least an imagein each of the red, blue, and green frequency bands.

At step 710, the system uses the machine learning system and the one ormore particular images to identify a plurality of pixels in the one ormore particular images as particular cloud locations. For example, theagricultural intelligence computer system may compute an output matrixfor one or more input matrices generated from the one or more imagesusing the trained machine learning system. The output matrix mayindicate, for each pixel, whether the pixel corresponds to a cloud or toa non-cloud location. In embodiments where the machine learning systemis configured to identify cloud shadows in addition to clouds, theoutput additionally indicates whether a pixel is a cloud shadow. In someembodiments, the output indicates a likelihood that the pixel is acloud, cloud shadow, or neither. Additionally or alternatively, asoftmax layer may be used to select a classification based on a highestprobability.

3.3. Cloud and Shadow Segmentation

In an embodiment, the server computer segments the identification of thecloud and the identification of the cloud shadow. For example, thesystem may utilize a first machine learning system to identify locationsof the clouds and, using the identified locations of the clouds,generate additional inputs for a second machine learning system to useto compute locations of cloud shadows. FIG. 9 depicts a method of usinga first machine learning system to identify pixels in an image as cloudlocations and a second machine learning system to identify pixels in animage as cloud shadow locations.

At step 902, images of agronomic fields produced using one or morefrequency bands are received. The agricultural intelligence computersystem may receive a plurality of sets of images directly or indirectlyfrom the satellite where each set of images comprises an image in eachfrequency band corresponding to a same location. At step 904,corresponding data identifying cloud and cloud shadow locations in theimages is received. The data may indicate, for each pixel of the imagesin a set of images, whether the pixel is a non-cloud/cloud shadow pixel,a cloud pixel, or a cloud shadow pixel.

In an embodiment, the agricultural intelligence computer systemadditionally receives metadata relating to the capture of each set ofimages. The metadata may indicate one or more of a time of imagecapture, a location of the satellite at the time of the image capture,an angle of the sun on the satellite at the time of the image capture, aheight of the satellite at the time of the image capture, or a distancebetween the satellite and the location to which the image corresponds.

At step 906, the system trains a first machine learning system toidentify cloud locations using the images as inputs and data identifyingpixels as cloud pixels or non-cloud pixels as outputs. The first machinelearning system may comprise any machine learning system which iscapable of accepting one or more matrices of values as inputs andgenerating one or more matrices of values as outputs, such as aconvolutional neural network. The system may train the first machinelearning system using matrices representing the one or more images asinputs and data identifying whether a pixel is a cloud or non-cloud asoutput. Thus, if the agricultural intelligence computer system receiveddata identifying each pixel as cloud, cloud shadow, or neither cloud orcloud shadow, the agricultural intelligence computer system may groupthe cloud shadow pixels in with the other non-cloud pixels.

At step 908, using the data identifying cloud locations, the systemidentifies a plurality of candidate cloud shadow locations. Forinstance, a two-dimensional image of a field with a cloud hovering overit could be taken from any number of angles with varying cloud heightand cloud size. The system may identify a plurality of possiblelocations for the cloud shadows in an image based on the locations ofthe cloud. For instance, the system may iterate over a plurality ofangles and heights to determine different possible sizes and locationsof cloud shadows.

In an embodiment, the system utilizes received metadata to decrease thenumber of variables in determining the locations of cloud shadows. Forexample, the metadata may identify the location of the field, locationand angle of the satellite, and angle of the sun at the time the imageis captured. The system may utilize the metadata to fix the satelliteand sun position with respect to the field. The system may then use theidentified cloud locations in the images to determine different possiblepositions of the cloud pictured in the image. Using the different cloudlocations, the system may identify a plurality of cloud shadowlocations. For example, cloud shadow locations may be computed as afunction of the sun location and the cloud's location and size.

In an embodiment, for each of the plurality of identified positions theidentified clouds, the system produces a cloud shadow map. The cloudshadow map includes a plurality of pixel values which indicate whetheror not the pixel is a cloud shadow. In an embodiment, the pixels in thecloud shadow map additionally indicate whether the pixel is a cloud.

FIG. 10 depicts a plurality of images comprising cloud shadow locationsgenerated based on varying assumptions of satellite angle and cloudheight. FIG. 10 includes maximum height assumption 1000, minimum heightassumption 1010, and interim height assumptions 1020.

Maximum height assumption 1000 comprises an assumption of thethree-dimensional cloud location and size at a predetermined maximumheight based on the two-dimensional identified cloud location in theimage. For instance, the predetermined maximum height may be 10,000meters. The system may determine the likely size and location of thecloud 1002 based on the position of the satellite, size of theidentified cloud in the image, and predetermined maximum height. Basedon the determined likely size and location of the cloud 1002 and thelocation and angle of the sun, the system may determine a likelylocation of the cloud shadow 1004. The system may then generate a cloudshadow map 1006 with pixels 1008 identifying the location of the cloudshadow 1004. For example, the system may generate a pixel map withvalues of 0 or 1 where each location with a value of 1 is a locationwhich the system has determined likely contains a cloud shadow and avalue of 0 corresponds to a location which the system has determineddoes not likely contain a cloud shadow.

Minimum height assumption 1010 comprises an assumption of thethree-dimensional cloud location and size at a predetermined minimumheight based on the two-dimensional identified cloud location in theimage. For instance, the predetermined minimum height may be 2,000meters. The system may determine the likely size and location of cloud1012 based on the position of the satellite, size of the identifiedcloud in the image, and predetermined minimum height. Based on thedetermined likely size and location of the cloud 1012 and the locationand angle of the sun, the system may determine a likely location of thecloud shadow 1014. The system may then generate a cloud shadow map 1016with pixels 1018 identifying the location of the cloud shadow 1014.

Interim height assumptions 1020 comprise a plurality of interimassumptions of the three-dimensional cloud location and size between themaximum height and the minimum height based on the two-dimensional cloudlocation in the image. For instance, the system may generate a newinterim assumption for every 2,000 meters of height between the minimumpredetermined height and the maximum predetermined height. The systemmay determine, for each interim assumption, the likely location of thecloud 1022 based on the position of the satellite, size of theidentified cloud in the image, and cloud height in the interimassumption. Based on the likely size and location of the cloud 1022 andthe location and angle of the sun, the system may determine a likelylocation of the cloud shadow 1024 for the interim assumption. The systemmay then generate a cloud shadow map 1026 with pixels 1028 identifyingthe location of the cloud shadow 1024.

While FIG. 10 depicts an example of iterating over one variable, i.e.the height of the cloud, embodiments may include iterations over aplurality of variables. For example, if the metadata includes an angleof the sun, but not the angle and/or location of the satellite at thetime of image capture, the system may iterate over both satellite angleand/or location and heights of the cloud. Thus, one candidate cloudshadow location may be generated based on an assumption of the lowestcloud height at the shallowest satellite angle, one may be generatedbased on an assumption of the highest cloud height at the shallowestsatellite angle, one may be generated based on an assumption of thelowest cloud height at the steepest satellite angle, and one may begenerated based on an assumption of the highest cloud height at thesteepest satellite angle.

Referring again to FIG. 9 , at step 910, the system trains a secondmachine learning system to identify cloud shadow locations using theimages and the candidate cloud shadow locations as input and dataidentifying cloud shadow locations as outputs. The second machinelearning system may comprise any machine learning system which iscapable of accepting one or more matrices of values as inputs andgenerating one or more matrices of values as outputs, such as aconvolutional neural network.

The system may train the second machine learning system using matricesrepresenting the one or more images and matrices representing theidentified candidate cloud shadow locations as inputs and dataidentifying whether a pixel is a cloud shadow or non-cloud shadow asoutput. For instance, if the first machine learning system was trainedusing training datasets comprising three stacked image matrices as theinputs and a matrix representing cloud locations as the output for eachtraining dataset, the second machine learning system may be trainedusing training datasets comprising ten stacked matrices as inputs and amatrix representing cloud shadow locations as the output for eachtraining dataset, the ten stacked matrices including the three stackedimage matrices and seven stacked candidate cloud location matrices. Inthis manner, the system utilizes relationships between clouds and cloudshadows to increase the efficacy of the machine learning model indetecting cloud shadows.

At step 912, one or more particular images of a particular agronomicfield produced using the one or more frequency bands are received. Theagricultural intelligence computer system may receive a set of imagesdirectly or indirectly from a satellite where the set of imagescomprises an image in each frequency band corresponding to the samelocation. In an embodiment, the received set of images includes an imagein each frequency band used to train the machine learning system. Forexample, if the machine learning system was trained using red, blue, andgreen frequency bands, the received set of images corresponding to thesame location may include at least an image in each of the red, blue,and green frequency bands.

At step 914, the system uses the first machine learning system and theone or more particular images to identify a plurality of pixels in theone or more particular images as particular cloud locations. Forexample, the agricultural intelligence computer system may compute anoutput matrix for input matrices generated from the one or moreparticular images using the trained first machine learning system. Theoutput matrix may indicate, for each pixel, whether the pixelcorresponds to a cloud or to a non-cloud location.

At step 916, the system identifies a plurality of particular candidatecloud shadow locations using the particular cloud locations. Forexample, the system may use the techniques described herein to iterateover a plurality of different cloud heights and sizes based on theparticular cloud locations to generate a plurality of cloud shadow maps,each cloud shadow map including one or more cloud shadow locations basedon a cloud height and size assumption generated from the identifiedparticular cloud locations.

At step 918, the system uses the second machine learning system, theplurality of particular candidate cloud shadow locations, and the one ormore particular images to identify a plurality of pixels in the one ormore particular images as particular cloud shadow locations. Forexample, the agricultural intelligence computer system may compute anoutput matrix for input matrices generated from the one or moreparticular images and the cloud shadow maps using the trained secondmachine learning system. The output matrix may indicate, for each pixel,whether the pixel corresponds to a cloud shadow or to a non-cloud shadowlocation.

4. Replacing Identified Cloud Pixels

In an embodiment, the system utilizes the systems and methods describedherein to improve images of agronomic fields by replacing pixels thatcomprise cloud or cloud shadows. For example, the system may remove allpixels that have been identified by cloud or cloud shadows from theimage. The system may then insert pixels into the locations where pixelshad been removed.

In an embodiment, the system uses additional images of the agronomiclocation to augment an image of an agronomic field where one or morepixels were removed for containing clouds and cloud shadows. Forexample, the agricultural intelligence computer system may receiveadditional images of the locations captured at a different time and/orday. The system may use the methods described herein to identify cloudsand cloud shadows in the additional images for removal. The system maythen identify non-cloud and non-cloud shadow pixels in the additionalimages that correspond to a same physical location as cloud or cloudshadow pixels in the initial image. The system may then fill the emptyspots of the initial image with the identified non-cloud and non-cloudshadow pixels from the additional images.

In an embodiment, the system interpolates pixel values from non-cloudand non-cloud shadow locations in an image to the pixels that wereremoved for containing cloud or cloud shadows. For example, the systemmay use one or more image interpolation algorithms to determine imagepixel values for removed pixels based on the image pixel values ofsurrounding pixels in the image. As another example, the system mayutilize a machine learning system trained using images with null valuesfor any cloud and cloud-shadow locations as inputs and images withidentified pixel values for the cloud and cloud shadow locations asoutputs to identify pixel values for the removed pixels.

5. Agronomic Modeling

In an embodiment, the system utilizes the systems and methods describedherein to improve agronomic modeling for agronomic fields in images withclouds and cloud shadows. For example, an agronomic yield model may beconfigured to model a yield of a crop as a function of at least anaverage normalized difference vegetation index (NDVI) value or otherindex values across the field. As the index values are generated fromimages of crops on a field, clouds and cloud shadows may negativelyimpact index values on those locations of the field. Thus, the systemmay ignore pixels that have been identified as containing clouds orcloud shadows when computing average index values for the purpose ofagronomic modeling. Additionally, the system may be configured toidentify the pixel as part of the field for other purposes, such astotal yield modeling, but without a pixel value. Thus, if the averagevegetative index is applied to all locations on the field, the pixel maybe given the average vegetative index value despite not being used forthe computation of the average vegetative index value.

In an embodiment, the system filters images for use in modeling based ondata identifying clouds and cloud shadows. For example, the system mayremove all images that contain clouds or cloud shadows and only useimages that are free of clouds or cloud shadows for modeling purposes.Removing the images containing clouds or cloud shadows may comprisegenerating, from a set of images of a field, a strict subset of imagesthat were not identified as containing clouds or cloud shadows.Additionally or alternatively, the system may remove images where aparticular portion of the images contain clouds or cloud shadows. Forexample, the system may remove all images where the percentage of pixelscontaining clouds or cloud shadows is greater than or equal to thirtypercent.

By removing images that include clouds or cloud shadows, the systemreduces memory usage of storing the images, reduces the number ofcomputations performed, and increases the accuracy of the modelingperformed using the remaining images. For example, if the systemreceives fifty images over a period of time for the purpose of modelingcrop yield, the system may first identify twenty of those images ascontaining more than a threshold percentage of clouds or cloud shadowsand remove the identified images. The system may then perform themodeling techniques using the remaining thirty images, therebydecreasing the computational cost of the modeling while increasing themodel's accuracy.

6. Agronomic Map Display

In an embodiment, the system utilizes the systems and methods describedherein to generate an improved display. For example, the system mayidentify ideal images of a field for display on a computing device basedon the data identifying clouds and cloud shadow locations. As apractical example, the system may request to display a recent image ofan agronomic field. The system may identify the most recently generatedimage of the field that does not contain any cloud or cloud shadow andcause display of the identified image. Additionally or alternatively,the system may identify the most recently generated image of the fieldthat contains less than a threshold percentage of cloud or cloud shadowand cause display of the identified image.

In an embodiment, the system generates an overlay for the image based onthe data identifying clouds and cloud shadows in the image. For example,the system may compute an index value, such as an NDVI value, for eachlocation on the field that has not been identified as including a cloudor cloud shadow. The system may then generate an overlay for the imagethat identifies the NDVI of the locations that do not include cloud orcloud shadow. Thus, if the system receives a request for display of acurrent image of a field with NDVI values, the system may use themethods described herein to identify cloud and cloud shadow locationsand generate an overlay for the remaining locations. The system may thencause display of the image with the overlay on a requesting clientcomputing device.

By generating an overlay that distinguishes between locations withclouds or cloud shadows and locations without, the system is able toprovide an improved interface which can display useful data overlayingan image of an agronomic field. Thus, the system may be able to displayimages from different times in a day or different days without losinginformation or providing incorrect information due to the existence of acloud. For example, the agricultural intelligence computer system mayreceive a request to display an image of an agronomic on a first day ata first time and, in response, display an image with an overlayidentifying vegetative index values for locations without clouds orcloud shadows. The system may then receive a second request to displayan image of the agronomic field on a second day at a second time and, inresponse, display an image with a different overlay due to the shiftingpositions of clouds and cloud shadows. In this manner, the systemprovides index values for locations where values are calculable in eachimage, thereby allowing a field manager to gain useful informationregarding an agronomic field despite the existence of clouds in multipleimages.

7. Benefits of Certain Embodiments

The systems and methods described herein provide a practical applicationof machine learning to solving a specific problem which arises in usingsatellite imagery for agronomic purposes. By utilizing a machinelearning algorithm, an agricultural intelligence computer system is ableto improve the utilization of images for agronomic purposes byidentifying clouds or cloud shadows in the images. This allows theagricultural intelligence computer system to practically apply machinelearning techniques to solve a problem unique to satellite imagery.Additionally, the methods and techniques described herein allow theagricultural intelligence computer system to generate improved images,improved agronomic models, and improved map displays.

The systems and methods described herein improve the computer's abilityto perform complex computations for purposes of agronomic modeling. Byremoving pixels including clouds and cloud shadows from received images,the system is able to produce accurate computations using a smallernumber of images, thereby reducing the number of computations thecomputer must perform to obtain the same accuracy in results.Additionally, memory of the computer system may be improved by removingfrom storage images which contain clouds and cloud shadows or imageswith more a threshold percentage of the image containing cloud or cloudshadows.

What is claimed is:
 1. A system comprising: one or more processors; amemory storing instructions executable by the one or more processors tocause the system to: receive a plurality of images of agronomic fieldsproduced using one or more frequency bands; receive data identifyingcloud locations and cloud shadow locations in the plurality of images;train a first machine learning system to identify cloud locations usingthe plurality of images as inputs and data identifying pixels as cloudpixels or non-cloud pixels as outputs; using the data identifying cloudlocations, identify a plurality of candidate cloud shadow locations foreach of the plurality of images, wherein identifying a plurality ofcandidate cloud locations comprises, for each image of the plurality ofimages: receiving metadata identifying a location of the agronomic fieldin the image; and using the metadata and the data identifying cloudlocations, identifying a plurality of possible physical locations ofclouds corresponding to the data identifying cloud locations byidentifying a first possible physical location of clouds at apredetermined maximum height, identifying a second possible physicallocation of clouds at a predetermined minimum height, and identifyingone or more interim possible physical locations of clouds at one or moreheights between the predetermined maximum height and the predeterminedminimum height; train a second machine learning system to identify cloudshadow locations using the images and the candidate cloud shadowlocations as inputs and data identifying pixels as cloud shadow pixelsor non-cloud shadow pixels as outputs; receive one or more particularimages of a particular agronomic field produced using the one or morefrequency bands; using the one or more particular images as inputs intothe first machine learning system, identify a plurality of pixels in theone or more particular images as particular cloud locations; using theparticular cloud locations, identify a plurality of particular candidatecloud shadow locations; using the one or more particular images and theplurality of particular candidate cloud shadow locations as inputs intothe second machine learning system, identify a plurality of pixels inthe one or more particular images as particular cloud shadow locations.2. The system of claim 1, wherein the machine learning system is aconvolutional encoder-decoder comprising a plurality of convolutionalencoding steps interspersed with pooling steps and a plurality ofconvolutional deciding steps interspersed with upsampling steps.
 3. Thesystem of claim 1, wherein the machine learning system is aconvolutional encoder-decoder that has been customized to accept inputsof different types using a customized machine learning layer whichshapes a build of a vector based on a symbolic shape of an outputtensor.
 4. The system of claim 1, wherein each input of the inputs usedto train the machine learning system comprise a plurality of stackedmatrices, each of the plurality of stacked matrices generated fromimages captured using different frequency bands.
 5. The system of claim1, comprising remove, from the one or more particular images, each pixelof the plurality of pixels which were identified as particular cloudlocations or particular cloud shadow locations.
 6. The system of claim5, wherein the instructions, when executed by the one or moreprocessors, further cause performance of: receiving one or more secondimages of the particular agronomic field produced using the one or morefrequency bands; using the one or more second image as inputs into firstmachine learning systems, identifying a plurality of pixels in the oneor more second images as second cloud locations; using the second cloudlocations, identifying a plurality of second candidate cloud shadowlocations; using the one or more second images and the plurality ofsecond candidate cloud shadow locations as inputs into the secondmachine learning system, identifying a plurality of pixels in the one ormore second images as second cloud shadow locations; identifying asubset of the plurality of pixels that were identified as particularcloud locations or cloud shadow locations in the one or more particularimages which correspond to a plurality of pixels that were notidentified as second cloud locations or second cloud shadow locations inthe one or more second images; replacing removed pixels corresponding tothe identified subset of the plurality of pixels that were identified asparticular cloud locations or particular cloud shadow locations in theone or more particular images with the plurality of pixels that were notidentified as second cloud locations or second cloud shadow locations inthe one or more second images.
 7. The system of claim 1, wherein theinstructions, when executed by the one or more processors, further causeof the system to: receive a plurality of second images of the particularagronomic field produced using the one or more frequency bands; usingthe plurality of second images as inputs into the machine learningsystem, identify a plurality of pixels in the plurality of second imagesas second cloud locations; using the second cloud locations, identify aplurality of second candidate cloud shadow locations; using theplurality of second images and the plurality of second candidate cloudshadow locations as inputs into the second machine learning system,identify a plurality of pixels in the plurality of second images assecond cloud shadow locations; receive a request to display an image ofthe particular agronomic field; determine that the one or moreparticular images comprise a number of pixels that were identified asparticular cloud locations or particular cloud shadow locations that isless than a number of pixels in each of the plurality of second imagesthat were identified as second cloud locations or second cloud shadowlocations; and in response, cause display of the one or more particularimages.
 8. A computer-implemented method comprising: receiving aplurality of images of agronomic fields produced using one or morefrequency bands; receiving data identifying cloud locations and cloudshadow locations in the plurality of images; training a first machinelearning system to identify cloud locations using the images as inputsand data identifying pixels as cloud pixels or non-cloud pixels asoutputs; using the data identifying cloud locations, identifying aplurality of candidate cloud shadow locations for each of the pluralityof images; training a second machine learning system to identify cloudshadow locations using the plurality of images and the candidate cloudshadow locations as inputs and data identifying pixels as cloud shadowpixels or non-cloud shadow pixels as outputs; receiving one or moreparticular images of a particular agronomic field produced using the oneor more frequency bands; using the one or more particular images asinputs into the first machine learning system, identifying a pluralityof pixels in the one or more particular images as particular cloudlocations; using the particular cloud locations, identifying a pluralityof particular candidate cloud shadow locations, wherein identifying aplurality of candidate cloud locations comprises, for each image of theplurality of images: receiving metadata identifying a location of anagronomic field in the image; using the metadata and the dataidentifying cloud locations, identifying a plurality of possiblephysical locations of clouds corresponding to the data identifying cloudlocations by identifying a first possible physical location of clouds ata predetermined maximum height, identifying a second possible physicallocation of clouds at a predetermined minimum height, and identifyingone or more interim possible physical locations of clouds at one or moreheights between the predetermined maximum height and the predeterminedminimum height; and using the one or more particular images and theplurality of particular candidate cloud shadow locations as inputs intothe second machine learning system, identifying a plurality of pixels inthe one or more particular images as particular cloud shadow locations.9. The computer-implemented method of claim 8, wherein the machinelearning system is a convolutional encoder-decoder comprising aplurality of convolutional encoding steps interspersed with poolingsteps and a plurality of convolutional deciding steps interspersed withupsampling steps.
 10. The computer-implemented method of claim 8,wherein the machine learning system is a convolutional encoder-decoderthat has been customized to accept inputs of different types using acustomized machine learning layer which shapes a build of a vector basedon a symbolic shape of an output tensor.
 11. The computer-implementedmethod of claim 8, wherein each input of the inputs used to train themachine learning system comprise a plurality of stacked matrices, eachof the plurality of stacked matrices generated from images capturedusing different frequency bands.
 12. The computer-implemented method ofclaim 8, further comprising removing, from the one or more particularimages, each pixel of the plurality of pixels which were identified asparticular cloud locations or particular cloud shadow locations.
 13. Thecomputer-implemented method of claim 12, comprising: receiving one ormore second images of the particular agronomic field produced using theone or more frequency bands; using the one or more second image asinputs into first machine learning systems, identifying a plurality ofpixels in the one or more second images as second cloud locations; usingthe second cloud locations, identifying a plurality of second candidatecloud shadow locations; using the one or more second images and theplurality of second candidate cloud shadow locations as inputs into thesecond machine learning system, identifying a plurality of pixels in theone or more second images as second cloud shadow locations; identifyinga subset of the plurality of pixels that were identified as particularcloud locations or cloud shadow locations in the one or more particularimages which correspond to a plurality of pixels that were notidentified as second cloud locations or second cloud shadow locations inthe one or more second images; replacing removed pixels corresponding tothe identified subset of the plurality of pixels that were identified asparticular cloud locations or particular cloud shadow locations in theone or more particular images with the plurality of pixels that were notidentified as second cloud locations or second cloud shadow locations inthe one or more second images.
 14. The computer-implemented method ofclaim 8, further comprising: receiving a plurality of second images ofthe particular agronomic field produced using the one or more frequencybands; using the plurality of second images as inputs into the machinelearning system, identifying a plurality of pixels in the plurality ofsecond images as second cloud locations; using the second cloudlocations, identifying a plurality of second candidate cloud shadowlocations; using the plurality of second images and the plurality ofsecond candidate cloud shadow locations as inputs into the secondmachine learning system, identifying a plurality of pixels in theplurality of second images as second cloud shadow locations; receiving arequest to display an image of the particular agronomic field;determining that the one or more particular images comprise a number ofpixels that were identified as particular cloud locations or particularcloud shadow locations that is less than a number of pixels in each ofthe plurality of second images that were identified as second cloudlocations or second cloud shadow locations and, in response, causingdisplay of the one or more particular images.